This course aims to provide students with a solid background in problem formulation and the fundamental statistical techniques and applications to data science. Topics include problem formulation, descriptive statistics, inferential statistics, confidence intervals, hypothesis testing, non-parametric statistics, experimental design, and categorical data. Emphasis will be placed on applying these techniques to large data sets. The language R will be used extensively. It is assumed that students will already have taken an undergraduate course in statistical techniques.
3 Credits

Data professionals use data to create powerful visuals in the form of dashboards and reports that tell a story. The ability to use software to visualize large datasets is a key skill in the field. This hands-on course will equip students with the skills to explore and visualize data using Tableau and visualization libraries in R and Python. Students will learn about advanced data preparation techniques that are essential to creating visual artifacts used for analysis and the decision-making process. Students will also learn to critically evaluate the effectiveness of designs and leverage visualization methods in various data related scenarios.
3 Credits

This course aims to provide students with a solid background in applications of Bayesian methods as well as regression and time series analysis at an advanced graduate level. The course covers the Bayesian methods for inference as well as basic techniques of both the linear model, generalized linear model, non-linear models, as well as various time series methods. Emphasis is placed on appropriate model selection, usage of statistical software, and interpretation of results. categorical data techniques, and bayesian methods for inference
Prerequisite: (STAT4230 or STAT4250 or STAT 5100) or approval from program director.
3 Credits

This course will cover the theory and practical application of machine learning algorithms. Students in this course will study supervised and unsupervised learning algorithms. Students will apply machine learning algorithms to datasets to uncover patterns using R and Python. Topics covered include random forest, support vector machines, deep neural networks, interpretable machine learning.
Prerequisites: INFS 7140 or INFS 6140
3 Credits

Modeling and Simulation covers a range of modeling techniques beyond what is covered in a regression or machine-learning course. This will include Optimization, Statistical Modeling in Robust Linear Regression, and Categorical Data Analysis. The course will also cover Simulation Techniques related to Statistical Estimation and Modeling. Examples include Monte Carlo techniques, Bootstrapping, and the EM algorithm. Students will use the R programming language to write their own code to carry out the above procedures.
Prerequisites: STAT 6050
3 Credits

This course concludes the Master of Science in Data Science degree program with a Data Science-related project (applied or research) of the instructor's and/or the student?s choice. This course focuses on advanced applications of Data Science techniques to solve complex real-world problems. The students can choose from learned techniques in Data Science, as well as venture into the latest, cutting-edge developments in Data Science. All projects must be pre-approved by the instructor.
Prerequisites: Completion of 15 credits. 3 Credits
3 Credits